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RCleanCommunity.R
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RCleanCommunity.R
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setwd("G:/My Drive/GrayLab/Projects/Plastics/ActiveProjects/RCleanCommunity/Data/Processed Data")
library(data.table)
library(dplyr)
library(RCurl)
library(tmap)
library(ggplot2)
library(tidyr)
library(scales)
library(viridis)
library(readxl)
LitteratiTrash <- fread("MyData2.csv")
Cleaned2018Data <- read_xlsx("CleanedData2018.xlsx")
Cleaned2018DataDateformatted <- Cleaned2018Data %>%
#mutate(TimeFormatted = ifelse(is.na(Time), NA, substr(Time,1,nchar(Time)-3))) %>%
mutate(DTFormatted = ifelse(is.na(DateTime), NA, sub("(.*)\\..*", "\\1", DateTime))) %>%
mutate(DTFormatted = ifelse(is.na(DTFormatted), NA, sub("(.*)\\+.*", "\\1", DTFormatted))) %>%
mutate(TimeFormatted = ifelse(is.na(Time), NA, sub("(.*)\\..*", "\\1", Time))) %>%
mutate(TimeFormatted = ifelse(is.na(TimeFormatted), NA, sub("(.*)\\+.*", "\\1", TimeFormatted))) %>%
mutate(DateAndTimeFormatted = ifelse(is.na(Date), NA, paste(as.character(Date), TimeFormatted, sep = " "))) %>%
mutate(LastDTFormat = ifelse(is.na(DateAndTimeFormatted), DTFormatted, DateAndTimeFormatted)) %>%
mutate(DateAndTime = as.POSIXct(LastDTFormat, format = "%Y-%m-%d %H:%M:%S")) %>%
select(ID, Longitude, Latitude, ImageURL, ImageThumbnail,DateAndTime, Material, Item, Brand)
write.csv(Cleaned2018DataDateformatted, "CleanedDateDTFormatted2018.csv")
#LitteratiTrash <- fread("CleanedData.csv")
LitteratiTrashWinMaterials <- LitteratiTrash %>%
mutate_all(funs(toupper)) %>%
mutate_all(funs(gsub(" ", "", ., fixed = TRUE))) %>%
group_by(Materials) %>%
summarise(count = n())
LitteratiTrashWinItems <- LitteratiTrash %>%
mutate_all(funs(toupper)) %>%
mutate_all(funs(gsub(" ", "", ., fixed = TRUE))) %>%
group_by(Items) %>%
summarise(count = n())
LitteratiTrashWinBrands <- LitteratiTrash %>%
mutate_all(funs(toupper)) %>%
mutate_all(funs(gsub(" ", "", ., fixed = TRUE))) %>%
group_by(Brands) %>%
summarise(count = n())
#Download Litterati Photos
Folder <- "/WinPhotos/"
for(photo in 1:nrow(LitteratiTrash)){
download.file(url = paste(LitteratiTrash[photo,5], sep = ""), destfile = paste(Folder, LitteratiTrash[photo,1], ".jpg", sep = ""), mode = "wb" )
Sys.sleep(0.1)
}
#download.file(url = "https://litterati-images-prod.s3-us-west-2.amazonaws.com/2018/09/28/cfa2c012-7756-3afe-9835-31047ebc71e0-92684.jpg", destfile = paste(Folder, photo, ".jpg", sep = ""), mode = "wb" )
###Make file for Lets Do It World
LDIW <- LitteratiTrash %>%
mutate_all(funs(toupper)) %>%
mutate_all(funs(gsub(" ", "", ., fixed = TRUE))) %>%
filter(Materials %in% c("PLASTIC", "PAPER", "GLASS", "METAL")) %>%
filter(Brands == "")
table(LitteratiTrash$Materials)
write.csv(LDIW, "LDIW.csv")
#Make Maps
tmap_mode('view')
# Example data("NLD_prov")
# Example tm_shape(NLD_prov) + tm_polygons('population') + tm_layout(basemaps = c('OpenStreetMap')) # Or basemap = 'https://mt1.google.com/vt/lyrs=m&x={x}&y={y}&z={z}'
pts <- cbind(LitteratiTrash$f)
tm_shape(Litterati) + tm_layout(basemaps = c('OpenStreetMap'))
#Analyze Litter Types
LitterTypes <- unlist(strsplit(LitteratiTrash$tags, ","))
LitterTypes <- as.data.frame(LitterTypes)
LitterTypes$Types <- LitterTypes$LitterTypes
LitterMaterial <- LitterTypes %>%
group_by(LitterTypes) %>%
summarise(n = n()) %>%
#ungroup()%>%
filter(LitterTypes == "plastic"| LitterTypes == "paper"| LitterTypes == "metal"| LitterTypes == "glass"| LitterTypes == "food") %>%
mutate(n = n/sum(n))
#ungroup()%>%
#group_by(n) %>%
#top_n(7,n)
LitterItem <- LitterTypes %>%
group_by(LitterTypes) %>%
summarise(n= n()) %>%
filter(LitterTypes == "cigarette"| LitterTypes == "wrapper"| LitterTypes == "napkin"| LitterTypes == "cup"| LitterTypes == "receipt") %>%
mutate(n = n/sum(n))
#ungroup()%>%
LitterBrand <- LitterTypes %>%
group_by(LitterTypes) %>%
summarise(n= n()) %>%
filter(LitterTypes == "marlboro"| LitterTypes == "jackinthebox"| LitterTypes == "camel"| LitterTypes == "starbucks"| LitterTypes == "mcdonalds") %>%
mutate(n = n/sum(n))
#ungroup()%>%
blank_theme <- theme_minimal()+
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.border = element_blank(),
panel.grid=element_blank(),
axis.ticks = element_blank(),
plot.title=element_text(size=14, face="bold")
)
LitterTypes$n <- LitterTypes$n/sum(LitterTypes$n)
ggplot(LitterTypes, aes(x="", y=n, fill=as.factor(as.integer(n*100))))+
geom_bar(width = 1, stat = "identity") + coord_polar("y", start=0) + scale_fill_viridis_d(direction = -1) + blank_theme +
theme(axis.text.x=element_blank()) +
geom_text(aes(y = n/7 + c(0, cumsum(n)[-length(n)])), label = (LitterTypes$LitterTypes), size=5)
ggplot(LitterMaterial, aes(x="", y=n, fill=as.factor(as.integer(n*100))))+
geom_bar(width = 1, stat = "identity") + coord_polar("y", start=0) + scale_fill_viridis_d(direction = -1) + blank_theme +
theme(axis.text.x=element_blank()) +
geom_text(aes(y = n/5 + c(0, cumsum(n)[-length(n)])), label = (LitterMaterial$LitterTypes), size=5)
ggplot(LitterBrand, aes(x="", y=n, fill=as.factor(as.integer(n*100))))+
geom_bar(width = 1, stat = "identity") + coord_polar("y", start=0) + scale_fill_viridis_d(direction = -1) + blank_theme +
theme(axis.text.x=element_blank()) +
geom_text(aes(y = n/5 + c(0, cumsum(n)[-length(n)])), label = (LitterBrand$LitterTypes), size=5)
ggplot(LitterItem, aes(x="", y=n, fill=as.factor(as.integer(n*100))))+
geom_bar(width = 1, stat = "identity") + coord_polar("y", start=0) + scale_fill_viridis_d(direction = -1) + blank_theme +
theme(axis.text.x=element_blank()) +
geom_text(aes(y = n/5 + c(0, cumsum(n)[-length(n)])), label = (LitterItem$LitterTypes), size=5)
#Get Weather Data
#Scraped from Wunderground
Weather <- read.csv("G:/My Drive/GrayLab/Plastics/Articles Publish/RCleanCommunity/WeatherWunderground.csv")
Weather$Year <- 2018
Weather$Date <- as.Date(paste(Weather$Year, "-", Weather$Month, "-", Weather$Day, sep= ""), "%Y-%m-%d")
Weather$Temperature <- Weather$Temp/max(Weather$Temp)
Weather$Precipatation <- Weather$Precipitation..in./max(Weather$Precipitation..in.)
Weather$`Wind Speed` <- Weather$Max.Wind..mph./max(Weather$Max.Wind..mph.)
Weather <- Weather %>%
select(Date, Temperature, Precipatation, `Wind Speed`) %>%
gather(Event, Intensity, -Date )
ggplot(Weather) + geom_line(aes(x=Date, y=Intensity, color = Event), size = 1.5) + theme_gray() + ylab("Maximum Normalized Intensity") + xlab("Date")
#Litter Generation Rate
runningdifferencedays <- function(x) {
for(value in 1:length(x)) {
x[value] - x[value -1]
}
}
for(value in 1:length(Win$Date)) {
if(value == 1) {
print(0)
}
else{
print(as.numeric(Win$Date)[value] - as.numeric(Win$Date)[value -1])
}
}
LitteratiTrash <- fread("MyData2.csv")
Win <- LitteratiTrash %>%
#filter(user_id == 92684) %>%
mutate(Date = substr(photo_timestamp,1,nchar(photo_timestamp)-3)) %>%
mutate(Date = as.Date(Date, "%Y-%m-%d %H:%M:%S" )) %>%
group_by(Date) %>%
summarise(Intensity = n()) %>%
ungroup() %>%
arrange(Date) %>%
mutate(DateDiff = as.numeric(Date) - lag(as.numeric(Date))) %>%
mutate(generationrate = Intensity/DateDiff)
ggplot(Win, aes(x=Date)) + geom_histogram(binwidth = 3, color = "black") + theme_gray() + ylab("Litter Count")
ggplot(Win, aes(x = Date, y = generationrate)) + geom_line() + theme_classic() + ylab("Litter Generation Rate (Count/Day)")
#Combined Plots
CombinedWetherTrash <- rbind(Weather, Win)
ggplot(CombinedWetherTrash) + geom_line(aes(x=Date, y=Intensity, color = Event), size = 1.5) + theme_gray() + scale_color_viridis_d()+ylab("Intensity") + xlab("Date")